Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations

Wanling Cai, Li CHEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.

Original languageEnglish
Title of host publicationUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages33-42
Number of pages10
ISBN (Electronic)9781450368612
DOIs
Publication statusPublished - 7 Jul 2020
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 14 Jul 202017 Jul 2020

Publication series

NameUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/TerritoryItaly
CityGenoa
Period14/07/2017/07/20

Scopus Subject Areas

  • Software

User-Defined Keywords

  • dialogue-based conversational recommender systems
  • intent taxonomy
  • user intent prediction
  • user satisfaction prediction

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